Abstract

Conventional solar cells are not economical and are recently too expensive to the manufacturers for extensive-scale electricity generation. Cost and efficiency is most vital factor in the accomplishment of any solar technology. In order to improve the conversion efficiency, the major research in third generation photovoltaic (PV) cells is directed toward retaining more sunlight using nanotechnology. Advancement in nanotechnology solar cell via quantum dots (QDs) could reduce the cost of PV cell and additionally enhance cell conversion efficiency. Silicon quantum dots (Si-QDs) are semiconductor nano crystals of nanometers dimension whose electron-holes are confined in all three spatial dimensions. Quantum dots have discrete electronic states. Quantum dots have capacity to change band gap with the adjustment in size of quantum dot. As the quantum dots size fluctuates over a wide range that demonstrates the variety of band gap so it will assimilate or discharge light. In this paper, the generic mathematical models of PV cell are adopted and then I-V and P-V characteristic curves are obtained from selected parameters using MATLAB software. The essential parameters are taken from datasheets. I-V and P-V characteristics curves are obtained for selected model. Silicon quantum dots have the tunable band gap that is added to conventional PV cell and obtain the I-V and P-V curves. After simulation, efficiency and power of Conventional PV cell to quantum dots based PV cell is compared. The property of quantum dots is used in extending the band gap of solar cells and increasing the maximum proportion of incident sunlight absorbed, hence improving efficiency.

Highlights

  • This paper describes a novel database for Urdu Text detection and recognition in natural scene images

  • Many natural scene images contain text in more than one language as well. This shows that if text recognition in natural scene images is carried for other languages, it could be helpful for foreign tourists to translate and understand what is written on road signboards, shop names, advertisement banners and product labels

  • The dataset contains more natural scene images as well as cropped words and characters, which shows that this dataset can be used as a benchmark for the Urdu text in natural scene images

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Summary

INTRODUCTION

Ext recognition in natural scene images has Tbecome a useful and challenging task in many real world applications. A novel dataset of Urdu images in natural scenes with cropped words and segmented characters containing Urdu text in advertisement banners, road sign. Some research work for isolated Arabic and Urdu character recognition in natural scene images has been reported and a dataset for Arabic scene text recognition has been developed [11]. This is the first benchmark for (a). The dataset is mainly targeted for isolated character and cropped word recognition in natural images It will further be increased for whole image text detection and end-to-end text extraction algorithms.

RELATED WORK
PROPOSED DATASET
SYNTHETIC URDU CHARACTER DATASET
CONCLUSIONS AND FUTURE WORK
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